55 citations · 283 across the 50 of their papers we have counts for
12 papers · 1 filter
Swallowing the Bitter Pill: Simplified Scalable Conformer Generation
Yuyang Wang, Ahmed A. Elhag, Navdeep Jaitly +2
We present a novel way to predict molecular conformers through a simple formulation that sidesteps many of the heuristics of prior works and achieves state of the art results by us…
Pseudo-Generalized Dynamic View Synthesis from a Video
Xiaoming Zhao, Alex Colburn, Fangchang Ma +3
Rendering scenes observed in a monocular video from novel viewpoints is a challenging problem. For static scenes the community has studied both scene-specific optimization techniqu…
Generative Modeling with Phase Stochastic Bridges
Tianrong Chen, Jiatao Gu, Laurent Dinh +3
Diffusion models (DMs) represent state-of-the-art generative models for continuous inputs. DMs work by constructing a Stochastic Differential Equation (SDE) in the input space (ie,…
Value function estimation using conditional diffusion models for control
Bogdan Mazoure, Walter Talbott, Miguel Angel Bautista +3
A fairly reliable trend in deep reinforcement learning is that the performance scales with the number of parameters, provided a complimentary scaling in amount of training data. As…
BOOT: Data-free Distillation of Denoising Diffusion Models with Bootstrapping
Jiatao Gu, Shuangfei Zhai, Yizhe Zhang +2
Diffusion models have demonstrated excellent potential for generating diverse images. However, their performance often suffers from slow generation due to iterative denoising. Know…
Transformers learn through gradual rank increase
Enric Boix-Adsera, Etai Littwin, Emmanuel Abbe +2
We identify incremental learning dynamics in transformers, where the difference between trained and initial weights progressively increases in rank. We rigorously prove this occurs…
PLANNER: Generating Diversified Paragraph via Latent Language Diffusion Model
Yizhe Zhang, Jiatao Gu, Zhuofeng Wu +3
Autoregressive models for text sometimes generate repetitive and low-quality output because errors accumulate during the steps of generation. This issue is often attributed to expo…
Manifold Diffusion Fields
Ahmed A. Elhag, Yuyang Wang, Joshua M. Susskind +1
We present Manifold Diffusion Fields (MDF), an approach that unlocks learning of diffusion models of data in general non-Euclidean geometries. Leveraging insights from spectral geo…
Control3Diff: Learning Controllable 3D Diffusion Models from Single-view Images
Jiatao Gu, Qingzhe Gao, Shuangfei Zhai +3
Diffusion models have recently become the de-facto approach for generative modeling in the 2D domain. However, extending diffusion models to 3D is challenging due to the difficulti…
MAST: Masked Augmentation Subspace Training for Generalizable Self-Supervised Priors
Chen Huang, Hanlin Goh, Jiatao Gu +1
Recent Self-Supervised Learning (SSL) methods are able to learn feature representations that are invariant to different data augmentations, which can then be transferred to downstr…
Stabilizing Transformer Training by Preventing Attention Entropy Collapse
Shuangfei Zhai, Tatiana Likhomanenko, Etai Littwin +5
Training stability is of great importance to Transformers. In this work, we investigate the training dynamics of Transformers by examining the evolution of the attention layers. In…
NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusion
Jiatao Gu, Alex Trevithick, Kai-En Lin +4
Novel view synthesis from a single image requires inferring occluded regions of objects and scenes whilst simultaneously maintaining semantic and physical consistency with the inpu…